Dr Mark Nasila, Chief Analytics Officer – FNB Chief Risk Office
In recent years, FNB Risk embarked on a data-powered strategic effort aimed at creating enhanced and interconnected insights across risk business units while driving efficiency, eliminating redundant activities, keep up with complex, evolving and emerging (Climate, cyber, financial crimes, model etc.) risks and freeing up risk and other professionals to do what they’re best at.
Many of the capabilities driving this strategy are enabled (and enhanced) by artificial intelligence (AI) and machine learning (ML), and it’s become clear they’re an inescapable part of the FNB risk function and its modernisation. Why? Because ML can extrapolate from experience, and AI is adept at parsing massive amounts of data while recognising patterns and flagging anomalies humans simply can’t.
Similarly, ML can help identify fraud, combat identity theft, and tackle other malfeasance by flagging behavioural discrepancies, sourced from massive datasets. ML and AI can also help with making predictions because everything learned from previous instances can be applied to future ones, and with a shrinking number of false positives.
AI and ML can also help combat the security risks that come from a growing number of internet-connected devices combined with globalisation — a combination that opens the door to a growing number of attacks from international adversaries.
AI and ML enable more than merely cost savings, though. They are enabling the risk function to build more desirable products for its customers and improve customer experience by, for example, streamlining the onboarding process and flagging problematic documents through AI enabled ID verification models at submission rather than after the fact. They also empower the Bank to improve our products and services over time by taking into account the ways customers use them, and any problems they experience along the way.
These new technologies also make real-time monitoring possible for FNB, along with deep insights and analytics previously impossible. At the same time, they remove the burden of manual, time-consuming assessments. On average, the use of AI frees up 70% of analysts’ time, generating a forensic synopsis ready for a human analyst to review that previously took hours can now be completed in as little as eight seconds.
That frees up the risk team to take deeper dives and identify root causes, while also positioning us to identify emerging risks and by leveraging our internally developed AI system we’re able to meet regulatory requirements and make forensic due diligence decisions faster, more accurately, and more efficiently. This AI system has been scaled to the rest of Africa, where it’s being used for fraud investigation, suspicious transaction reporting, and even to enable the risk advisory space.
AI is also being used for business intelligence by enabling the automation of risk event insights and risk decisioning. Risks worthy of escalation can be identified more accurately and rapidly, and business unit-specific risks can be identified, while the learnings from them can be transferred to other business units.
At the same time, these sorts of risk models are proving useful outside of customer interactions. For example, AI and algorithmic risk assessment can be invaluable in the realm of climate risk assessment where myriad variables need to be considered in tandem with one another. The forecasts will enable new strategies and business models that can account for climate risk, something that’s previously been arduous or impossible.
The FNB Risk data literacy programme – aimed at empowering every member of the organisation with the requisite skills to turn data into actionable insights hasn’t only enabled a growing number of our colleagues to harness AI, ML, and other emerging technologies to contend with existing challenges, but it is empowering them to prepare for new ones. The programme has been running for the past 18 months and was designed to empower the entire risk workforce to make data-informed decisions, regardless of their role or previous experience with data analysis.
The successes outlined above are a direct result of the strategic objectives laid out three years ago in the risk data strategy. These included using data to enhance risk management practices and proactively identify risks breaches outside of risk appetite and tolerance limits, automating manual risk management processes, driving effective data risk aggregation and controls, and enabling end-to-end data governance.
Over the next 18 months, these same mechanisms will enable the next frontier of continued risk data asset creation, management, and data-driven risk decisioning. They’ll enable us to build further and scale our AI capabilities, increase AI and data analytics literacy across the group (especially for risk), drive collaborative engagement across FNB, and position FNB to continue reimagining risk in the future, and solve for as-yet unforeseen risks.